Introduction to ggplot2

class: center, middle, inverse, title-slide

# Introduction to ggplot2
### Antoine & Nicolas
### cynkra GmbH
### February 24th, 2022

---

# Basics for visualisation in R using {ggplot2}

* {ggplot2} is the most used R visualization package
* "gg" stands for the "grammar of graphics"
* It is shipped with {tidyverse}

```r
library(tidyverse)
```

```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
```

```
## ✓ ggplot2 3.3.5          ✓ purrr   0.3.4     
## ✓ tibble  3.1.6          ✓ dplyr   1.0.8.9000
## ✓ tidyr   1.1.4          ✓ stringr 1.4.0     
## ✓ readr   2.1.0          ✓ forcats 0.5.1
```

```
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
```

???

In the {tidyverse} the standard package for visualization is {ggplot2}.
The functions of this package follow a quite unique logic (the "grammar of graphics") and therefore require a special syntax.
In this section we want to give a short introduction, how to get started with {ggplot2}.

---

# Grammar of graphics

* Grammar: A set of structural rules which help define and establish the component of a language
* Grammar of graphics : framework to describe the components of any graphics

With ggplot we don't create different plots by calling completely different functions,
instead we recognize the universal quality of some components.

A more general approach means there's a learning curve, but it pays off!

---

# Our dataset

```r
mpg
```

```
## # A tibble: 234 × 11
##   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class 
##   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr> 
## 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
## 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
## 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
## 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
## # … with 230 more rows
```

* displ : engine displacement, in litres
* cyl : number of cylinders
* hwy : highway miles per gallon
* drv: drive train, f = front-wheel drive, r = rear wd, 4 = 4wd
* cty : city miles per gallon
* class : "type" of car

---

# Creating the plot skeleton

The `ggplot()` function is used to set up the chart

```r
ggplot(data = mpg, mapping = aes(x = displ, y = hwy))
```

1. Choose dataset (argument `data`) : `mpg`
2. Map variables to visual properties (argument `mapping`) :
  - map `displ` to `x` (horizontal coord)
  - map `hwy` to `y` (horizontal coord)
3. Create a "ggplot" object containing this information, that can be enhanced to display a useful chart when it's printed

---

# Creating the plot skeleton

We map `displ` to the `x` coordinate, and `hwy` to the `y` coordinate,
but we haven't mentioned what we want to see yet!

```r
ggplot(mpg, aes(x = displ, y = hwy))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-4-1.png)

???

This created only an empty plot, because we did not tell {ggplot2} which geometry we want to use to display the variables we set in the `ggplot()` call.
We do this by adding (with the help of the `+` operator after the `ggplot()`-call) a different function starting with `geom_` to provide this information.

---

# Adding a layer

To create a useful chart we need to add layers

* There are different types of layers
* Layers are added using the `+` operator
* `geom_point()` creates a scatter plot layer

```r
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point()
```

???

This is maybe the most basic plot you can create.
To map a different variable than `disp` to the x-axis, change the respective variable name in the `aes()` argument.

---

# Adding a layer

```r
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-6-1.png)

* `geom_*()` functions define the **geometry** of a layer
* Visual properties `x` and `y` are called aesthetics, different geoms recogize different
 aesthetics

???

This is maybe the most basic plot you can create.
To map a different variable than `disp` to the x-axis, change the respective variable name in the `aes()` argument.

---

# Teaser #1

We have more aesthetics!

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-7-1.png)

---

# Teaser #2

We can combine "geom" layers

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-8-1.png)

---

# Teaser #3

And we can do much more!

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point() +
  geom_smooth(se = FALSE) +
  facet_wrap(vars(drv)) +
  theme_bw() +
  labs(title = "Highway miles per gallon vs engine displacement",  
       subtitle = "for different drive train types")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-9-1.png)

???

Always good to have: The *ggplot2* cheatsheet (https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf).

---

# `geom_*` functions

"geom" = geometric object, see documentation : https://ggplot2.tidyverse.org/reference/#section-layer-geoms

```r
ls("package:ggplot2", pattern = "^geom_")
```

```
##  [1] "geom_abline"            "geom_area"              "geom_bar"              
##  [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
##  [7] "geom_boxplot"           "geom_col"               "geom_contour"          
## [10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
## [13] "geom_curve"             "geom_density"           "geom_density_2d"       
## [16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
## [19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
## [22] "geom_freqpoly"          "geom_function"          "geom_hex"              
## [25] "geom_histogram"         "geom_hline"             "geom_jitter"           
## [28] "geom_label"             "geom_line"              "geom_linerange"        
## [31] "geom_map"               "geom_path"              "geom_point"            
## [34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
## [37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
## [40] "geom_rect"              "geom_ribbon"            "geom_rug"              
## [43] "geom_segment"           "geom_sf"                "geom_sf_label"         
## [46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
## [49] "geom_step"              "geom_text"              "geom_tile"             
## [52] "geom_violin"            "geom_vline"
```

---

# `geom_*` functions

The most popular ones are

- `geom_point()`
- `geom_line()`
- `geom_bar()`
- `geom_col()`
- `geom_histogram()`
- `geom_boxplot()`
- `geom_text()` / `geom_label()`

---

# `geom_point()`

We know a bit about it now, we'll use it to show case some features that can
be generalized to other features.

---

# `geom_point()`

In `?geom_point` we find which aesthetics are available :

- `x`
- `y`
- `alpha`
- `colour` (or `color`)
- `fill`
- `group`
- `shape`
- `size`
- `stroke`

---

# `geom_point()`

We can run `vignette("ggplot2-specs")` to know more about them, the main ones are:

- `x` (required) : horizontal coordinate
- `y` (required) : vertical coordinate
- `colour` (or `color`) : color of contour or point
- `shape` : shape of the point
- `size` : size of the point
- `alpha` : opacity (low = more transparent)

---

# `geom_point()`

`color` **mapping**

To map a variable to an aesthetics we use `aes()` in the `mapping` argument of
`ggplot()`

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-11-1.png)

---

# `geom_point()`

`color` **setting**

We set an aesthetic to a constant value if we provide it in the geom
function as an independent parameter.

```r
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(color = "blue")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-12-1.png)

---

# `geom_point()`

`color` mapping or setting ?

Oops! what happened here ?

```r
ggplot(mpg, aes(x = displ, y = hwy, color = "blue")) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-13-1.png)

---

# `geom_point()`

Some aesthetis like `color` can take both discrete and continuous values

```r
ggplot(mpg, aes(x = displ, y = hwy, color = cty)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-14-1.png)

---

# `geom_point()`

`shape` should be mapped to a discrete variable

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv, shape = drv)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-15-1.png)

---

# `geom_point()`

but `shape` can also be defined with the same symbol for all points in the plot

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(shape = "triangle") # we can also provide an integer value
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-16-1.png)

---

# `geom_point()`

`size` should be mapped to a continuous variable

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv, size = cty)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-17-1.png)

---

# `geom_point()`

We can also set a specific `size` for all points in the plot

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(size = 8)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-18-1.png)

---

# `geom_point()`

`alpha` is most useful when set as a constant, to improve visualizations
when overplotting

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(size = 8, alpha = 0.3)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-19-1.png)

---

# `geom_point()`

`geom_*()` functions have a `position` argument to adjust the position of geometric
objects.
A common use for `geom_point()` is `position = "jitter"`, also good to handle
overplotting!

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(position = "jitter")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-20-1.png)

---

# `geom_point()`

`position = "jitter"` and  
`position = position_jitter()` are equivalent,
but `position_jitter()` offers more control through arguments

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point(position = position_jitter(width = .5, height = .5))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-21-1.png)

---

# Exercise

Using the `iris` dataset available in R by default and the knowledge from the previous slides, create a scatter plot using 2 numeric columns, and use `Species` as a color. You can use the pattern we used above.

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-22-1.png)

---

# `geom_line()`

using `?geom_line` we find which aesthetics are available, the main ones are:

- `x` (required) : horizontal coordinate
- `y` (required) : vertical coordinate
- `alpha` : opacity (low = more transparent)
- `colour` (or `color`) : color of the line
- `linetype` : type of the line
- `size` : size of the line

---

# `geom_line()`

`color` and `linetype`

We can use a constant value for these aesthetics if we provide it in the geom function.

```r
ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_line(color = "blue", linetype = "dotdash")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-23-1.png)

you can see the different line types using `?linetype`

---

# `geom_line()`

`linetype` should be mapped to a discrete variable

```r
ggplot(mpg, aes(x = displ, y = hwy, linetype = drv)) +
  geom_line()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-24-1.png)

---
# `geom_line()`

using `size` you can change how thick are the lines

```r
ggplot(mpg, aes(x = displ, y = hwy, linetype = drv)) +
  geom_line(size=2)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-25-1.png)

---

# `geom_line()`

Exercise :

Using the `economics` dataset available in R by default and the knowledge from the previous slides, draw a plot using `geom_line` and different aesthetics mapped to variables. Change the color and use a dashed linetype.

---

# `geom_bar()`

using `?geom_bar` we find which aesthetics are available, the main ones are:

you should use only one coordinate `x` or `y`

- `x` : use it to create vertical bars
- `y` : use it to create horizontal bars
- `fill` : define bar colors
- `alpha` : opacity (low = more transparent)

---

# `geom_bar()`

`fill` should be mapped to a discrete variable. When the coordinate (`x` or `y`) and `fill` are using the *same* variable each category has its own color.

```r
ggplot(mpg, aes(x = drv, fill = drv)) +
  geom_bar()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-26-1.png)

---

# `geom_bar()`

When the coordinate (`x`or `y`) and `fill` are using a *different* variable, each bar represents the distribution of the variable used in `fill` for each category of the coordinate (`x`or `y`), creating stacked bars.

```r
ggplot(mpg, aes(x = drv, fill = class)) +
  geom_bar()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-27-1.png)

---

# `geom_bar()`

`geom_bar()` is often used with `position = "dodge"` (equivalent to `position = position_dodge()`), and this parameter changes the plot from stacked bars to clustered bars.

```r
ggplot(mpg, aes(x = drv, fill = class)) +
  geom_bar(position = "dodge")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-28-1.png)

---

# `geom_bar()`

`position = "fill"` is useful too!

```r
ggplot(mpg, aes(x = drv, fill = class)) +
  geom_bar(position = "fill")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-29-1.png)

---

# `geom_bar()`

Exercise :

Using the `diamonds` dataset and the knowledge from the previous slides, draw a barplot using different aesthetics mapped to variables. Use different positions to compare the plots.

---

# `geom_col()`

```r
df <- data.frame(category = c("a", "b", "c"), value = c(2.3, 1.9, 3.2))
ggplot(df, aes(x = category, y = value)) +
  geom_col(fill = "darkblue")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-30-1.png)

---

# `geom_histogram()`

.pull-left[

```r
ggplot(mpg, aes(x = displ)) +
  geom_histogram(fill = "darkblue", bins = 10)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-31-1.png)

]
  
  .pull-right[

```r
ggplot(mpg, aes(x = displ, fill = drv)) +
  geom_histogram(bins = 10)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-32-1.png)

]

---

# `geom_boxplot()`

`geom_boxplot()` needs one variable to be of class `character` or `factor` (better) to initiate the grouping.

```r
ggplot(mpg, aes(x = class, y = displ)) +
  geom_boxplot()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-33-1.png)

---

# `geom_text()`/`geom_label()`

These work a lot like `geom_point()`, but with the added aesthetic `label`

```r
avgs <-
  mpg %>% 
  group_by(drv) %>% 
  summarize(displ = mean(displ), hwy = mean(hwy))

ggplot(avgs, aes(x = displ, y = hwy, label = drv, color = drv)) +
  geom_label()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-34-1.png)

---

# `geom_text()`/`geom_label()`

These work a lot like `geom_point()`, but with the added aesthetic `label`

```r
avgs <-
  mpg %>% 
  group_by(drv) %>% 
  summarize(displ = mean(displ), hwy = mean(hwy))

ggplot(avgs, aes(x = displ, y = hwy, label = drv)) +
  geom_text(color = "black")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-35-1.png)

---

# Inheritance

`geom_*` functions need data and aesthetics, if they are not provided directly
they are inherited fron the object created by `ggplot()`. This is what we've done
so far and is mostly recommended but the following works

```r
ggplot() +
  geom_point(aes(x = displ, y = hwy, color = drv), mpg)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-36-1.png)

---

# Inheritance

A use case

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv))  +
  geom_point() +
  geom_label(data = avgs, aes(label = drv), nudge_y = 3, show.legend = FALSE)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-37-1.png)

---

# Summary : What makes a layer ?

A layer combines :

* **data**, provided through the `data` argument
* A **geometric object**, that we define by choosing the relevant `geom_*()` function
* The **mapping** between data variables and visual components ("aesthetics"),
  provided through the `mapping` argument
* A **statistical transformation** to apply on variables, provided by the `stat` argument 
(usually defaults are what we need, not covered in this course)
* A **position** adjustment in case of overlapping data, provided through the `position`
 argument and `position_*()` functions

---

# Legend, and labels

Using `labs` you can set the title/subtitle and also add a caption and a tag.

.pull-left[

```r
ggplot(
  data = mpg,
  mapping = aes(x = displ, y = hwy, color = class)
) +
  geom_point() +
  labs(
    x = "Displacement",
    y = "Highway mileage\n[miles per gallon]",
    color = "Car class",
    title = "Highway mileages depending on displacement",
    subtitle = "By car class"
  )
```
]

.pull-right[
![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-39-1.png)
  ]

---

# Legend, and labels

Exercise :

Using the `iris` dataset and the knowledge from the previous slides, draw a boxplot using different aesthetics mapped to variables, and also change the labels and title.

---

# Legend, and labels

There are often several ways of doing thing in {ggplot2}

.pull-left[

```r
ggplot(
  data = mpg,
  mapping = aes(x = displ, y = hwy, color = class)
) +
  geom_point() +
  xlab("Displacement") +
  ylab("Highway mileage\n[miles per gallon]") +
  ggtitle("Highway mileages depending on displacement", "By car class") +
  scale_color_discrete(guide = guide_legend("Car class")) # more complex, more control!
```
]

.pull-right[
![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-41-1.png)
  ]
  
---

# `scale_*()` functions

Scales control the details of how data values are translated to visual properties
There is a plethora of such functions, their name follow the `scale_<aes>_*()` pattern. 
For example to change the scale of an axis to a logarithmic scale:

.pull-left[

```r
ggplot(
  data = mpg,
  mapping = aes(x = displ, y = hwy, color = class)
) +
  geom_point() +
  scale_x_log10()
```
]

More: https://stackoverflow.com/questions/70942728

.pull-right[
  
![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-43-1.png)
  ]
  
---

# Facetting

“Facetting” denotes an idea of dividing a graphic into sub-graphics based on the (categorical) values of one or more variables of a dataset. Therefore, each sub-graphic shows a plot for a subset of the data.

`facet_grid()` and `facet_wrap()` provide 2 ways of facetting:

.pull-left[

```r
facet_grid(facets = vars(<variable>), 
           scales = "fixed", 
           ...)

facet_wrap(rows = vars(<variable>), 
           cols = vars(<variable>), 
           scales = "fixed", 
           ...)
```
]

.pull-right[
<img src="data:image/png;base64,#fig/facets.png" width="800"/>
]

---

#  `facet_wrap` function

with `?facet_wrap` we can find the different arguments to change our plot.

- `facets` (required) : set of categorical variables
- `dir` : `"h"` (default) for horizontal, or `"v"`, for vertical orientation.
- `nrow` or `ncol` : 	Number of rows and columns.

---

#  `facet_wrap` direction

.pull-left[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = drv)) +
  facet_wrap(vars(year))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-45-1.png)
]

.pull-right[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = drv)) +
  facet_wrap(vars(year), dir = "v")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-46-1.png)
]

---

#  `facet_wrap` `nrow` or `ncol`

.pull-left[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = drv)) +
  facet_wrap(vars(class))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-47-1.png)
]

.pull-right[

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = drv)) +
  facet_wrap(vars(class), nrow = 2)
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-48-1.png)
]

---

#  `facet_grid` function

While `facet_wrap()` tries to act smart and hide non-existing combinations of sub-plots, `facet_grid()` will create a full matrix of sub-plots for all possible combinations.

Most of the time when using only one categorical variable, `facet_wrap()` does a good job and is preferred over `facet_grid()`. However, `facet_grid` might be preferred in the following cases:

- when faceting over >= 2 variables

- when plots of empty combinations should be shown

Let’s compare `facet_grid` and `facet_wrap` for 2 grouping variables.

---

#  `facet_grid` for 2 grouping variables

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  facet_grid(vars(year), vars(cyl))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-49-1.png)

---

#  `facet_wrap` for 2 grouping variables

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point(aes(colour = class)) +
  facet_wrap(vars(year, cyl))
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-50-1.png)

---

# Themes

The `theme()` function is used to customize the non-data components of your plots:

* titles
* labels
* fonts
* background
* gridlines
* legends

---

# Themes

`theme()` has a lot of arguments (95!), which themselves often need to be defined
using other functions. You'll have to read the doc!

OR use the simpler `theme_*()` wrapper functions documented in `?ggtheme`.
These have good defaults and a limited amount of arguments easy to specify.

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv))  +
  geom_point() + 
  theme_classic()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-51-1.png)

---

# Themes

`theme()` has a lot of arguments (95!), which themselves often need to be defined
using other functions. You'll have to read the doc!

OR use the simpler `theme_*()` wrapper functions documented in `?ggtheme`.
These have good defaults and a limited amount of arguments easy to specify.

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv))  +
  geom_point() + 
  theme_light()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-52-1.png)

---

# Themes

`theme()` has a lot of arguments (95!), which themselves often need to be defined
using other functions. You'll have to read the doc!

OR use the simpler `theme_*()` wrapper functions documented in `?ggtheme`.
These have good defaults and a limited amount of arguments easy to specify.

```r
ggplot(mpg, aes(x = displ, y = hwy, color = drv))  +
  geom_point() + 
  theme_minimal()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-53-1.png)

---

# Export & saving

The default way to export plots is by using `ggsave()`.

It differs slightly from other “exporting” functions in R because it comes with some smart defaults (see `?ggsave()`)

.pull-left[

```r
ggplot(mtcars, aes(mpg, wt)) +
  geom_point()
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-54-1.png)
]

.pull-right[

```r
ggsave("mtcars.pdf")
```

```r
ggsave("mtcars.png")
```
]
  
---

## Extensions

Many package extend {ggplot2} with new themes, palettes, geoms, and even ways to make your 
charts interactive or animated.

A selection :

{ggforce}: https://ggforce.data-imaginist.com/

{patchwork}: https://patchwork.data-imaginist.com/

{ggtext}: https://github.com/clauswilke/ggtext

{ggiraph}: http://davidgohel.github.io/ggiraph

{plotly}: https://plotly-r.com/

{ggbeeswarm}: https://github.com/eclarke/ggbeeswarm

{esquisse}: https://dreamrs.github.io/esquisse

---

# Extensions

You can find more extensions in:

[exts.ggplot2.tidyverse.org](https://exts.ggplot2.tidyverse.org/gallery/)

---

# Extensions: {esquisse}

{esquisse} is a fantastic tool to create charts from a UI, copy and paste into your own code and learn a lot about ggplot.

Reproduce this plot with `esquisse::esquisser(palmerpenguins::penguins)`

```r
ggplot(palmerpenguins::penguins) +
  aes(x = island, y = body_mass_g, fill = species) +
  geom_boxplot() +
  theme_classic() + 
  theme(legend.position = "bottom")
```

![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-57-1.png)

---

# Extensions: {patchwork}

It combines separated plots, and the data source can be different.

.pull-left[

```r
library(patchwork)

p1 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  labs(
    title = "Plot using mpg dataset"
  )

p2 <- ggplot(economics, aes(x = date, y = pce)) + 
  geom_line() +
  labs(
    title = "Plot using economics dataset"
  )

p1 + p2
```
]

.pull-right[
![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-59-1.png)
  ]

---

# Extensions: {patchwork}

The combination of plots can have different layouts:

.pull-left[

```r
p1 <- ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point()
p2 <- ggplot(economics, aes(x = date, y = pce)) + 
  geom_line()
p3 <- ggplot(mpg, aes(x = displ)) +
  geom_histogram(fill = "darkblue", bins = 10)
p4 <- ggplot(mpg, aes(x = class, y = displ)) +
  geom_boxplot()

(p1 | p2 | p3) /
      p4
```
]

.pull-right[
![](data:image/png;base64,#tidy_intro_day3_2_ggplot-i_files/figure-html/unnamed-chunk-61-1.png)
]